elasticsearch/docs/reference/ml/anomaly-detection/apis/close-job.asciidoc

[role="xpack"]
[testenv="platinum"]
[[ml-close-job]]
= Close {anomaly-jobs} API
++++
<titleabbrev>Close jobs</titleabbrev>
++++

Closes one or more {anomaly-jobs}.

[[ml-close-job-request]]
== {api-request-title}

`POST _ml/anomaly_detectors/<job_id>/_close` +

`POST _ml/anomaly_detectors/<job_id>,<job_id>/_close` +

`POST _ml/anomaly_detectors/_all/_close` +

[[ml-close-job-prereqs]]
== {api-prereq-title}

* Requires the `manage_ml` cluster privilege. This privilege is included in the 
`machine_learning_admin` built-in role.

[[ml-close-job-desc]]
== {api-description-title}

A job can be opened and closed multiple times throughout its lifecycle.

A closed job cannot receive data or perform analysis operations, but you can
still explore and navigate results.

You can close multiple {anomaly-jobs} in a single API request by using a group
name, a comma-separated list of jobs, or a wildcard expression. You can close
all jobs by using `_all` or by specifying `*` as the `<job_id>`.

If you close an {anomaly-job} whose {dfeed} is running, the request first tries
to stop the {dfeed}. This behavior is equivalent to calling
<<ml-stop-datafeed,stop {dfeed}>> with the same `timeout` and `force` parameters
as the close job request.

When you close a job, it runs housekeeping tasks such as pruning the model history,
flushing buffers, calculating final results and persisting the model snapshots.
Depending upon the size of the job, it could take several minutes to close and
the equivalent time to re-open.

After it is closed, the job has a minimal overhead on the cluster except for
maintaining its meta data. Therefore it is a best practice to close jobs that
are no longer required to process data.

When a {dfeed} that has a specified end date stops, it automatically closes
its associated job.

NOTE: If you use the `force` query parameter, the request returns without performing
the associated actions such as flushing buffers and persisting the model snapshots.
Therefore, do not use this parameter if you want the job to be in a consistent state
after the close job API returns. The `force` query parameter should only be used in
situations where the job has already failed, or where you are not interested in
results the job might have recently produced or might produce in the future.

[[ml-close-job-path-parms]]
== {api-path-parms-title}

`<job_id>`::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection-wildcard]

[[ml-close-job-query-parms]]
== {api-query-parms-title}

`allow_no_match`::
(Optional, Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-jobs]

`force`::
  (Optional, Boolean) Use to close a failed job, or to forcefully close a job
  which has not responded to its initial close request.

`timeout`::
  (Optional, <<time-units, time units>>) Controls the time to wait until a job 
  has closed. The default value is 30 minutes.

[[ml-close-job-response-codes]]
== {api-response-codes-title}

`404` (Missing resources)::
  If `allow_no_match` is `false`, this code indicates that there are no 
  resources that match the request or only partial matches for the request.

[[ml-close-job-example]]
== {api-examples-title}

[source,console]
--------------------------------------------------
POST _ml/anomaly_detectors/low_request_rate/_close
--------------------------------------------------
// TEST[skip:Kibana sample data]

When the job is closed, you receive the following results:

[source,console-result]
----
{
  "closed": true
}
----